Thesis Abstract

**Abstract
** The increasing reliance on networked systems in various domains has highlighted the critical need for effective anomaly detection mechanisms to safeguard against cyber threats. This thesis focuses on the development of a machine learning algorithm tailored for real-time anomaly detection in network traffic. The proposed algorithm aims to enhance the security posture of networked systems by accurately identifying and mitigating anomalous activities that could indicate potential security breaches. The research begins by exploring the current landscape of anomaly detection techniques in network security and identifying the limitations and challenges faced by existing approaches. Through a comprehensive review of relevant literature, the study establishes a solid foundation for the development of an innovative machine learning algorithm that overcomes the shortcomings of traditional methods. Chapter 3 outlines the research methodology employed in this study, detailing the data collection process, feature selection techniques, model training and evaluation methodologies, and performance metrics used to assess the effectiveness of the proposed algorithm. The methodology is designed to ensure the robustness and reliability of the developed anomaly detection system. Chapter 4 presents a detailed discussion of the findings obtained from the experimental evaluation of the machine learning algorithm. The results demonstrate the efficacy of the proposed approach in accurately detecting anomalies in real-time network traffic data, showcasing its potential to significantly enhance the security posture of networked systems. Finally, Chapter 5 provides a comprehensive summary of the research outcomes and concludes the thesis by highlighting the key contributions, implications, and future directions for further research in the field of real-time anomaly detection in network traffic. The study underscores the significance of leveraging machine learning techniques to bolster network security and emphasizes the critical role of advanced anomaly detection mechanisms in safeguarding against evolving cyber threats. In conclusion, the development of an effective machine learning algorithm for real-time anomaly detection in network traffic represents a vital contribution to the field of cybersecurity. By leveraging advanced data analytics and machine learning capabilities, this research aims to empower organizations to proactively identify and respond to anomalous activities, thereby fortifying their cyber defenses and ensuring the integrity and reliability of networked systems in the face of evolving cyber threats.

Thesis Overview